Adaptive filter theory:
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Upper Saddle River, NJ
Prentice Hall
2002
|
| Edition: | 4. ed. |
| Series: | Prentice Hall information and systems sciences series
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturverz. S. 870 - 911 |
| Physical Description: | XVI, 920 S. graph. Darst. |
| ISBN: | 0130901261 |
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| 245 | 1 | 0 | |a Adaptive filter theory |c Simon Haykin |
| 250 | |a 4. ed. | ||
| 264 | 1 | |a Upper Saddle River, NJ |b Prentice Hall |c 2002 | |
| 300 | |a XVI, 920 S. |b graph. Darst. | ||
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| 490 | 0 | |a Prentice Hall information and systems sciences series | |
| 500 | |a Literaturverz. S. 870 - 911 | ||
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| adam_text | ADAPTIVE FILTER THEORY FOURTH EDITION SIMON HAYKIN COMMUNICATIONS
RESEARCH LABORATORY MCMASTER UNIVERSITY HAMILTON, ONTARIO, CANADA FRONT
ICE HALL PRENTICE HALL UPPER SADDLE RIVER, NEW JERSEY 07458 CONTENTS
PREFACE X ACKNOWLEDGMENTS XV BACKGROUND AND PREVIEW 1 1. THE FILTERING
PROBLEM 1 2. LINEAR OPTIMUM FILTERS 3 3. ADAPTIVE FILTERS 4 4. LINEAR
FILTER STRUCTURES 6 5. APPROACHES TO THE DEVELOPMENT OF LINEAR ADAPTIVE
FILTERS 14 6. ADAPTIVE BEAMFORMING 18 7. FOUR CLASSES OF APPLICATIONS 22
8. HISTORICAL NOTES 25 CHAPTER 1 STOCHASTIC PROCESSES AND MODELS 35 1.1
PARTIAL CHARACTERIZATION OF A DISCRETE-TIME STOCHASTIC PROCESS 35 1.2
MEAN ERGODIC THEOREM 37 1.3 CORRELATION MATRIX 39 1.4 CORRELATION MATRIX
OF SINE WAVE PLUS NOISE 43 1.5 STOCHASTIC MODELS 45 1.6 WOLD
DECOMPOSITION 51 1.7 ASYMPTOTIC STATIONARITY OF AN AUTOREGRESSIVE
PROCESS 53 1.8 YULE-WALKER EQUATIONS 55 1.9 COMPUTER EXPERIMENT:
AUTOREGRESSIVE PROCESS OF ORDER TWO 57 1.10 SELECTING THE MODEL ORDER 65
1.11 COMPLEX GAUSSIAN PROCESSES 67 1.12 POWER SPECTRAL DER/SITY 69 1.13
PROPERTIES OF POWER SPECTRAL DENSITY 71 1.14 TRANSMISSION OF A
STATIONARY PROCESS THROUGH A LINEAR FILTER 73 1.15 CRAMER SPECTRAL
REPRESENTATION FOR A STATIONARY PROCESS 76 1.16 POWER SPECTRUM
ESTIMATION 78 1.17 OTHER STATISTICAL CHARACTERISTICS OF A STOCHASTIC
PROCESS 81 1.18 POLYSPECTRA 82 1.19 SPECTRAL-CORRELATION DENSITY 85 1.20
SUMMARY 88 PROBLEMS 89 CHAPTER 2 WIENER FILTERS 94 2.1 LINEAR OPTIMUM
FILTERING: STATEMENT OF THE PROBLEM 94 2.2 PRINCIPLE OF ORTHOGONALITY 96
IV CONTENTS 2.3 MINIMUM MEAN-SQUARE ERROR 100 2.4 WIENER-HOPF EQUATIONS
102 2.5 ERROR-PERFORMANCE SURFACE 104 2.6 MULTIPLE LINEAR REGRESSION
MODEL 108 2.7 EXAMPLE 110 2.8 LINEARLY CONSTRAINED MINIMUM-VARIANCE
FILTER 115 2.9 GENERALIZED SIDELOBE CANCELLERS 120 2.10 SUMMARY 126
PROBLEMS 128 CHAPTER 3 LINEAR PREDICTION 136 3.1 FORWARD LINEAR
PREDICTION 136 3.2 BACKWARD LINEAR PREDICTION 142 3.3 LEVINSON-DURBIN
ALGORITHM 148 3.4 PROPERTIES OF PREDICTION-ERROR FILTERS 156 3.5
SCHUR-COHNTEST 166 3.6 AUTOREGRESSIVE MODELING OF A STATIONARY
STOCHASTIC PROCESS 168 3.7 CHOLESKY FACTORIZATION 171 3.8 LATTICE
PREDICTORS 174 3.9 ALL-POLE, ALL-PASS LATTICE FILTER 179 3.10
JOINT-PROCESS ESTIMATION 181 3.11 PREDICTIVE MODELING OF SPEECH 185 3.12
SUMMARY 192 PROBLEMS 193 CHAPTER 4 METHOD OF STEEPEST DESCENT 203 4.1
BASIC IDEA OF THE STEEPEST-DESCENT ALGORITHM 203 4.2 THE
STEEPEST-DESCENT ALGORITHM APPLIED TO THE WIENER FILTER 204 4.3
STABILITY OF THE STEEPEST-DESCENT ALGORITHM 208 4.4 EXAMPLE 213 4.5 THE
STEEPEST-DESCENT ALGORITHM AS A DETERMINISTIC SEARCH METHOD 225 4.6
VIRTUE AND LIMITATION OF THE STEEPEST-DESCENT ALGORITHM 226 4.7 SUMMARY
227 PROBLEMS 228 CHAPTER 5 LEAST-MEAN-SQUARE ADAPTIVE FILTERS 231 5.1
OVERVIEW OF THE STRUCTURE AND OPERATION OF THE LEAST-MEAN-SQUARE
ALGORITHM 231 5.2 LEAST-MEAN-SQUARE ADAPTATION ALGORITHM 235 5.3
APPLICATIONS 238 5.4 STATISTICAL LMS THEORY 258 5.5 COMPARISON OF THE
LMS ALGORITHM WITH THE STEEPEST-DESCENT ALGORITHM 278 5.6 COMPUTER
EXPERIMENT ON ADAPTIVE PREDICTION 279 5.7 COMPUTER EXPERIMENT ON
ADAPTIVE EQUALIZATION 285 5.8 COMPUTER EXPERIMENT ON A MINIMUM-VARIANCE
DISTORTIONLESS-RESPONSE BEAMFORMER 291 5.9 » DIRECTIONALITY OF
CONVERGENCE OF THE LMS ALGORITHM FOR NONWHITE INPUTS 293 5.10 ROBUSTNESS
OF THE LMS FILTER: H* CRITERION 297 5.11 UPPER BOUNDS ON THE STEP-SIZE
PARAMETER FOR DIFFERENT SCENARIOS 306 5.12 TRANSFER FUNCTION APPROACH
FOR DETERMINISTIC INPUTS 307 5.13 SUMMARY 311 PROBLEMS 312 VI CONTENTS
CHAPTER 6 NORMALIZED LEAST-MEAN-SQUARE ADAPTIVE FILTERS 320 6.1
NORMALIZED LMS FILTER AS THE SOLUTION TO A CONSTRAINED OPTIMIZATION
PROBLEM 320 6.2 STABILITY OF THE NORMALIZED LMS FILTER 324 6.3 STEP-SIZE
CONTROL FOR ACOUSTIC ECHO CANCELLATION 327 6.4 GEOMETRIC CONSIDERATIONS
PERTAINING TO THE CONVERGENCE PROCESS FOR REAL-VALUED DATA 331 6.5
AFFINE PROJECTION ADAPTIVE FILTERS 334 6.6 SUMMARY 340 PROBLEMS 341
CHAPTER 7 FREQUENCY-DOMAIN AND SUBBAND ADAPTIVE FILTERS 344 7.1
BLOCK-ADAPTIVE FILTERS 345 7.2 FAST BLOCK-LMS ALGORITHM 350 7.3
UNCONSTRAINED FREQUENCY-DOMAIN ADAPTIVE FILTERS 355 7.4
SELF-ORTHOGONALIZING ADAPTIVE FILTERS 356 7.5 COMPUTER EXPERIMENT ON
ADAPTIVE EQUALIZATION 367 7.6 SUBBAND ADAPTIVE FILTERS 372 7.7
CLASSIFICATION OF ADAPTIVE FILTERING ALGORITHMS 380 7.8 SUMMARY 381
PROBLEMS 382 CHAPTER 8 METHOD OF LEAST SQUARES 385 8.1 STATEMENT OF THE
LINEAR LEAST-SQUARES ESTIMATION PROBLEM 385 8.2 DATA WINDOWING 388 8.3
PRINCIPLE OF ORTHOGONALITY REVISITED 389 8.4 MINIMUM SUM OF ERROR
SQUARES 392 8.5 NORMAL EQUATIONS AND LINEAR LEAST-SQUARES FILTERS 393
8.6 TIME-AVERAGE CORRELATION MATRIX 396 8.7 REFORMULATION OF THE NORMAL
EQUATIONS IN TERMS OF DATA MATRICES 398 8.8 PROPERTIES OF LEAST-SQUARES
ESTIMATES 402 8.9 MVDR SPECTRUM ESTIMATION 406 8.10 REGULARIZED MVDR
BEAMFORMING 409 8.11 SINGULAR-VALUE DECOMPOSITION 414 8.12 PSEUDOINVERSE
421 8.13 INTERPRETATION OF SINGULAR VALUES AND SINGULAR VECTORS 423 8.14
MINIMUM-NORM SOLUTION TO THE LINEAR LEAST-SQUARES PROBLEM 424 8.15
NORMALIZED LMS ALGORITHM VIEWED AS THE MINIMUM-NORM SOLUTION TO AN
UNDERDETERMINED LEAST-SQUARES ESTIMATION PROBLEM 427 8.16 SUMMARY 429
PROBLEMS 430 CHAPTER 9 RECURSIVE LEAST-SQUARES ADAPTIVE FILTERS 436 9.1
SOME PRELIMINARIES 436 9.2 THE MATRIX INVERSION LEMMA 440 9.3 THE
EXPONENTIALLY WEIGHTED RECURSIVE LEAST-SQUARES ALGORITHM 440 9.4
SELECTION OF THE REGULARIZING PARAMETER 444 9.5 UPDATE RECURSION FOR THE
SUM OF WEIGHTED ERROR SQUARES 446 9.6 EXAMPLE: SINGLE-WEIGHT ADAPTIVE
NOISE CANCELLER 447 9.7 CONVERGENCE ANALYSIS OF THE RLS ALGORITHM 448
9.8 COMPUTER EXPERIMENT ON ADAPTIVE EQUALIZATION 454 9.9 ROBUSTNESS
OTRLS FILTERS 457 9.10 SUMMARY 463 PROBLEMS 463 CONTENTS VII CHAPTER 10
KALMAN FILTERS 466 10.1 RECURSIVE MINIMUM MEAN-SQUARE ESTIMATION FOR
SCALAR RANDOM VARIABLES 466 10.2 STATEMENT OF THE KALMAN FILTERING
PROBLEM 470 10.3 THE INNOVATIONS PROCESS 472 10.4 ESTIMATION OF THE
STATE USING THE INNOVATIONS PROCESS 474 10.5 FILTERING 479 10.6 INITIAL
CONDITIONS 483 10.7 SUMMARY OF THE KALMAN FILTER 483 10.8 KALMAN FILTER
AS THE UNIFYING BASIS FOR RLS FILTERS 485 10.9 VARIANTS OF THE KALMAN
FILTER 491 10.10 THE EXTENDED KALMAN FILTER 496 10.11 SUMMARY 501
PROBLEMS 501 CHAPTER 11 SQUARE-ROOT ADAPTIVE FILTERS 506 11.1
SQUARE-ROOT KALMAN FILTERS 506 11.2 BUILDING SQUARE-ROOT ADAPTIVE
FILTERS ON THEIR KALMAN FILTER COUNTERPARTS 512 11.3 QR-RLS ALGORITHM
513 11.4 ADAPTIVE BEAMFORMING 521 11.5 INVERSE QR-RLS ALGORITHM 528 11.6
SUMMARY 531 PROBLEMS 531 CHAPTER 12 ORDER-RECURSIVE ADAPTIVE FILTERS 535
12.1 GRADIENT-ADAPTIVE LATTICE FILTER 536 12.2 ORDER-RECURSIVE ADAPTIVE
FILTERS USING LEAST-SQUARES ESTIMATION: AN OVERVIEW 543 12.3 ADAPTIVE
FORWARD LINEAR PREDICTION 544 12.4 ADAPTIVE BACKWARD LINEAR PREDICTION
548 12.5 CONVERSION FACTOR 550 12.6 LEAST-SQUARES LATTICE PREDICTOR 553
12.7 ANGLE-NORMALIZED ESTIMATION ERRORS 563 12.8 FIRST-ORDER STATE-SPACE
MODELS FOR LATTICE FILTERING 565 12.9 QR-DECOMPOSITION-BASED
LEAST-SQUARES LATTICE FILTERS 571 12.10 FUNDAMENTAL PROPERTIES OF THE
QRD-LSL FILTER 579 12.11 COMPUTER EXPERIMENT ON ADAPTIVE EQUALIZATION
581 12.12 RECURSIVE LEAST-SQUARES LATTICE FILTERS USING A POSTERIORI
ESTIMATION ERRORS 586 12.13 RECURSIVE LSL FILTERS USING A PRIORI
ESTIMATION ERRORS WITH ERROR FEEDBACK 592 12.14 RELATION BETWEEN
RECURSIVE LSL AND RLS FILTERS 596 12.15 SUMMARY 598 PROBLEMS 600 CHAPTER
13 FINITE-PRECISION EFFECTS 607 13.1 QUANTIZATION ERRORS 608 13.2
LEAST-MEAN-SQUARE ALGORITHM 610 13.3 RECURSIVE LEAST-SQUARES ALGORITHM
619 13.4 SQUARE-ROOT ADAPTIVE FILTERS 625 13.5 ORDER-RECURSIVE ADAPTIVE
FILTERS 627 13.6 FAST TRANSVERSAL FILTERS 629 13.7 * SUMMARY 633
PROBLEMS 635 CHAPTER 14 TRACKING OF TIME-VARYING SYSTEMS 637 14.1 MARKOV
MODEL FOR SYSTEM IDENTIFICATION 637 14.2 DEGREE OF NONSTATIONARITY 640
VIII CONTENTS 14.3 CRITERIA FOR TRACKING ASSESSMENT 642 14.4 TRACKING
PERFORMANCE OF THE LMS ALGORITHM 643 14.5 TRACKING PERFORMANCE OF THE
RLS ALGORITHM 647 14.6 COMPARISON OF THE TRACKING PERFORMANCE OF LMS AND
RLS ALGORITHMS 651 14.7 HOW TO IMPROVE THE TRACKING BEHAVIOR OF THE RLS
ALGORITHM 654 14.8 COMPUTER EXPERIMENT ON SYSTEM IDENTIFICATION 657 14.9
AUTOMATIC TUNING OF THE ADAPTATION CONSTANTS 659 14.10 SUMMARY 664
PROBLEMS 665 CHAPTER 15 ADAPTIVE FILTERS USING INFINITE-DURATION IMPULSE
RESPONSE STRUCTURES 666 15.1 IIR ADAPTIVE FILTERS: OUTPUT ERROR METHOD
666 15.2 IIR ADAPTIVE FILTERS: EQUATION ERROR METHOD 671 15.3 SOME
PRACTICAL CONSIDERATIONS 673 15.4 LAGUERRE TRANSVERSAL FILTERS 674 15.5
ADAPTIVE LAGUERRE LATTICE FILTERS 677 15.6 SUMMARY 681 PROBLEMS 682
CHAPTER 16 BLIND DECONVOLUTION 684 16.1 AN OVERVIEW OF THE BLIND
DECONVOLUTION PROBLEM 684 16.2 CHANNEL IDENTIFIABILITY USING
CYCLOSTATIONARY STATISTICS 688 16.3 SUBSPACE DECOMPOSITION FOR
FRACTIONALLY SPACED BLIND IDENTIFICATION 689 16.4 BUSSGANG ALGORITHM FOR
BLIND EQUALIZATION 703 16.5 EXTENSION OF THE BUSSGANG ALGORITHM TO
COMPLEX BASEBAND CHANNELS 720 16.6 SPECIAL CASES OF THE BUSSGANG
ALGORITHM 721 16.7 FRACTIONALLY SPACED BUSSGANG EQUALIZERS 725 16.8
SUMMARY 729 PROBLEMS 732 CHAPTER 17 BACK-PROPAGATION LEARNING 736 17.1
SIGMOID NEURONAL MODEL 736 17.2 MULTILAYER PERCEPTRON 738 17.3 COMPLEX
BACK-PROPAGATION ALGORITHM 740 17.4 UNIVERSAL APPROXIMATION THEOREM 753
17.5 NETWORK COMPLEXITY 755 17.6 TEMPORAL PROCESSING: HOW TO ACCOUNT FOR
TIME 756 17.7 VIRTUES AND LIMITATIONS OF BACK-PROPAGATION LEARNING 758
17.8 SUMMARY 759 PROBLEMS 760 EPILOGUE 762 1. PROPORTIONATE ADAPTATION
762 2. ROBUST STATISTICS 764 3. BLIND SOURCE SEPARATION 766 4. RECURRENT
NEURAL NETWORKS 770 5. NONLINEAR DYNAMICAL SYSTEMS: DERIVATIVE-FREE
STATE ESTIMATION 773 APPENDIX A COMPLEX VARIABLES 779 A.I
CAUCHY-REIMAJIN EQUATIONS 779 A.2 CAUCHY S INTEGRAL FORMULA 781 A.3
LAURENT S SERIES 783 CONTENTS IX A.4 SINGULARITIES AND RESIDUES 785 A.5
CAUCHY S RESIDUE THEOREM 786 A.6 PRINCIPLE OF THE ARGUMENT 787 A.7
INVERSION INTEGRAL FOR THE Z-TRANSFORM 790 A.8 PARSEVAL S THEOREM 792
APPENDIX B DIFFERENTIATION WITH RESPECT TO A VECTOR 794 B.1 BASIC
DEFINITIONS 794 B.2 EXAMPLES 796 B.3 RELATION BETWEEN THE DERIVATIVE
WITH RESPECT TO A VECTOR AND THE GRADIENT VECTOR 798 APPENDIX C METHOD
OF LAGRANGE MULTIPLIERS 799 C.I OPTIMIZATION INVOLVING A SINGLE EQUALITY
CONSTRAINT 799 C.2 OPTIMIZATION INVOLVING MULTIPLE EQUALITY CONSTRAINTS
800 C.3 OPTIMUM BEAMFORMER 801 APPENDIX D ESTIMATION THEORY 802 D.I
LIKELIHOOD FUNCTION 802 D.2 CRAM6R-RAO INEQUALITY 803 D.3 PROPERTIES OF
MAXIMUM-LIKELIHOOD ESTIMATORS 804 D.4 CONDITIONAL MEAN ESTIMATOR 805
APPENDIX E EIGENANALYSIS 807 E.I THE EIGENVALUE PROBLEM 807 E.2
PROPERTIES OF EIGENVALUES AND EIGENVECTORS 809 E.3 LOW-RANK MODELING 823
E.4 EIGENFILTERS 827 E.5 EIGENVALUE COMPUTATIONS 829 APPENDIX F
ROTATIONS AND REFLECTIONS 833 F.I PLANE ROTATIONS 833 F.2 TWO-SIDED
JACOBI ALGORITHM 835 F.3 CYCLIC JACOBI ALGORITHM 841 F.4 HOUSEHOLDER
TRANSFORMATION 844 F.5 THE QR ALGORITHM 847 APPENDIX G COMPLEX WISHART
DISTRIBUTION 854 G.I DEFINITION 854 G.2 THE CHI-SQUARE DISTRIBUTION AS A
SPECIAL CASE 855 G.3 PROPERTIES OF THE COMPLEX WISHART DISTRIBUTION 856
G.4 EXPECTATION OF THE INVERSE CORRELATION MATRIX ^^(N) 857 GLOSSARY 858
BIBLIOGRAPHY 870 INDEX 912
|
| adam_txt |
ADAPTIVE FILTER THEORY FOURTH EDITION SIMON HAYKIN COMMUNICATIONS
RESEARCH LABORATORY MCMASTER UNIVERSITY HAMILTON, ONTARIO, CANADA FRONT
ICE HALL PRENTICE HALL UPPER SADDLE RIVER, NEW JERSEY 07458 CONTENTS
PREFACE X ACKNOWLEDGMENTS XV BACKGROUND AND PREVIEW 1 1. THE FILTERING
PROBLEM 1 2. LINEAR OPTIMUM FILTERS 3 3. ADAPTIVE FILTERS 4 4. LINEAR
FILTER STRUCTURES 6 5. APPROACHES TO THE DEVELOPMENT OF LINEAR ADAPTIVE
FILTERS 14 6. ADAPTIVE BEAMFORMING 18 7. FOUR CLASSES OF APPLICATIONS 22
8. HISTORICAL NOTES 25 CHAPTER 1 STOCHASTIC PROCESSES AND MODELS 35 1.1
PARTIAL CHARACTERIZATION OF A DISCRETE-TIME STOCHASTIC PROCESS 35 1.2
MEAN ERGODIC THEOREM 37 1.3 CORRELATION MATRIX 39 1.4 CORRELATION MATRIX
OF SINE WAVE PLUS NOISE 43 1.5 STOCHASTIC MODELS 45 1.6 WOLD
DECOMPOSITION 51 1.7 ASYMPTOTIC STATIONARITY OF AN AUTOREGRESSIVE
PROCESS 53 1.8 YULE-WALKER EQUATIONS 55 1.9 COMPUTER EXPERIMENT:
AUTOREGRESSIVE PROCESS OF ORDER TWO 57 1.10 SELECTING THE MODEL ORDER 65
1.11 COMPLEX GAUSSIAN PROCESSES 67 1.12 POWER SPECTRAL DER/SITY 69 1.13
PROPERTIES OF POWER SPECTRAL DENSITY 71 1.14 TRANSMISSION OF A
STATIONARY PROCESS THROUGH A LINEAR FILTER 73 1.15 CRAMER SPECTRAL
REPRESENTATION FOR A STATIONARY PROCESS 76 1.16 POWER SPECTRUM
ESTIMATION 78 1.17 OTHER STATISTICAL CHARACTERISTICS OF A STOCHASTIC
PROCESS 81 1.18 POLYSPECTRA 82 1.19 SPECTRAL-CORRELATION DENSITY 85 1.20
SUMMARY 88 PROBLEMS 89 CHAPTER 2 WIENER FILTERS 94 2.1 LINEAR OPTIMUM
FILTERING: STATEMENT OF THE PROBLEM 94 2.2 PRINCIPLE OF ORTHOGONALITY 96
IV CONTENTS 2.3 MINIMUM MEAN-SQUARE ERROR 100 2.4 WIENER-HOPF EQUATIONS
102 2.5 ERROR-PERFORMANCE SURFACE 104 2.6 MULTIPLE LINEAR REGRESSION
MODEL 108 2.7 EXAMPLE 110 2.8 LINEARLY CONSTRAINED MINIMUM-VARIANCE
FILTER 115 2.9 GENERALIZED SIDELOBE CANCELLERS 120 2.10 SUMMARY 126
PROBLEMS 128 CHAPTER 3 LINEAR PREDICTION 136 3.1 FORWARD LINEAR
PREDICTION 136 3.2 BACKWARD LINEAR PREDICTION 142 3.3 LEVINSON-DURBIN
ALGORITHM 148 3.4 PROPERTIES OF PREDICTION-ERROR FILTERS 156 3.5
SCHUR-COHNTEST 166 3.6 AUTOREGRESSIVE MODELING OF A STATIONARY
STOCHASTIC PROCESS 168 3.7 CHOLESKY FACTORIZATION 171 3.8 LATTICE
PREDICTORS 174 3.9 ALL-POLE, ALL-PASS LATTICE FILTER 179 3.10
JOINT-PROCESS ESTIMATION 181 3.11 PREDICTIVE MODELING OF SPEECH 185 3.12
SUMMARY 192 PROBLEMS 193 CHAPTER 4 METHOD OF STEEPEST DESCENT 203 4.1
BASIC IDEA OF THE STEEPEST-DESCENT ALGORITHM 203 4.2 THE
STEEPEST-DESCENT ALGORITHM APPLIED TO THE WIENER FILTER 204 4.3
STABILITY OF THE STEEPEST-DESCENT ALGORITHM 208 4.4 EXAMPLE 213 4.5 THE
STEEPEST-DESCENT ALGORITHM AS A DETERMINISTIC SEARCH METHOD 225 4.6
VIRTUE AND LIMITATION OF THE STEEPEST-DESCENT ALGORITHM 226 4.7 SUMMARY
227 PROBLEMS 228 CHAPTER 5 LEAST-MEAN-SQUARE ADAPTIVE FILTERS 231 5.1
OVERVIEW OF THE STRUCTURE AND OPERATION OF THE LEAST-MEAN-SQUARE
ALGORITHM 231 5.2 LEAST-MEAN-SQUARE ADAPTATION ALGORITHM 235 5.3
APPLICATIONS 238 5.4 STATISTICAL LMS THEORY 258 5.5 COMPARISON OF THE
LMS ALGORITHM WITH THE STEEPEST-DESCENT ALGORITHM 278 5.6 COMPUTER
EXPERIMENT ON ADAPTIVE PREDICTION 279 5.7 COMPUTER EXPERIMENT ON
ADAPTIVE EQUALIZATION 285 5.8 COMPUTER EXPERIMENT ON A MINIMUM-VARIANCE
DISTORTIONLESS-RESPONSE BEAMFORMER 291 5.9 » DIRECTIONALITY OF
CONVERGENCE OF THE LMS ALGORITHM FOR NONWHITE INPUTS 293 5.10 ROBUSTNESS
OF THE LMS FILTER: H* CRITERION 297 5.11 UPPER BOUNDS ON THE STEP-SIZE
PARAMETER FOR DIFFERENT SCENARIOS 306 5.12 TRANSFER FUNCTION APPROACH
FOR DETERMINISTIC INPUTS 307 5.13 SUMMARY 311 PROBLEMS 312 VI CONTENTS
CHAPTER 6 NORMALIZED LEAST-MEAN-SQUARE ADAPTIVE FILTERS 320 6.1
NORMALIZED LMS FILTER AS THE SOLUTION TO A CONSTRAINED OPTIMIZATION
PROBLEM 320 6.2 STABILITY OF THE NORMALIZED LMS FILTER 324 6.3 STEP-SIZE
CONTROL FOR ACOUSTIC ECHO CANCELLATION 327 6.4 GEOMETRIC CONSIDERATIONS
PERTAINING TO THE CONVERGENCE PROCESS FOR REAL-VALUED DATA 331 6.5
AFFINE PROJECTION ADAPTIVE FILTERS 334 6.6 SUMMARY 340 PROBLEMS 341
CHAPTER 7 FREQUENCY-DOMAIN AND SUBBAND ADAPTIVE FILTERS 344 7.1
BLOCK-ADAPTIVE FILTERS 345 7.2 FAST BLOCK-LMS ALGORITHM 350 7.3
UNCONSTRAINED FREQUENCY-DOMAIN ADAPTIVE FILTERS 355 7.4
SELF-ORTHOGONALIZING ADAPTIVE FILTERS 356 7.5 COMPUTER EXPERIMENT ON
ADAPTIVE EQUALIZATION 367 7.6 SUBBAND ADAPTIVE FILTERS 372 7.7
CLASSIFICATION OF ADAPTIVE FILTERING ALGORITHMS 380 7.8 SUMMARY 381
PROBLEMS 382 CHAPTER 8 METHOD OF LEAST SQUARES 385 8.1 STATEMENT OF THE
LINEAR LEAST-SQUARES ESTIMATION PROBLEM 385 8.2 DATA WINDOWING 388 8.3
PRINCIPLE OF ORTHOGONALITY REVISITED 389 8.4 MINIMUM SUM OF ERROR
SQUARES 392 8.5 NORMAL EQUATIONS AND LINEAR LEAST-SQUARES FILTERS 393
8.6 TIME-AVERAGE CORRELATION MATRIX 396 8.7 REFORMULATION OF THE NORMAL
EQUATIONS IN TERMS OF DATA MATRICES 398 8.8 PROPERTIES OF LEAST-SQUARES
ESTIMATES 402 8.9 MVDR SPECTRUM ESTIMATION 406 8.10 REGULARIZED MVDR
BEAMFORMING 409 8.11 SINGULAR-VALUE DECOMPOSITION 414 8.12 PSEUDOINVERSE
421 8.13 INTERPRETATION OF SINGULAR VALUES AND SINGULAR VECTORS 423 8.14
MINIMUM-NORM SOLUTION TO THE LINEAR LEAST-SQUARES PROBLEM 424 8.15
NORMALIZED LMS ALGORITHM VIEWED AS THE MINIMUM-NORM SOLUTION TO AN
UNDERDETERMINED LEAST-SQUARES ESTIMATION PROBLEM 427 8.16 SUMMARY 429
PROBLEMS 430 CHAPTER 9 RECURSIVE LEAST-SQUARES ADAPTIVE FILTERS 436 9.1
SOME PRELIMINARIES 436 9.2 THE MATRIX INVERSION LEMMA 440 9.3 THE
EXPONENTIALLY WEIGHTED RECURSIVE LEAST-SQUARES ALGORITHM 440 9.4
SELECTION OF THE REGULARIZING PARAMETER 444 9.5 UPDATE RECURSION FOR THE
SUM OF WEIGHTED ERROR SQUARES 446 9.6 EXAMPLE: SINGLE-WEIGHT ADAPTIVE
NOISE CANCELLER 447 9.7 CONVERGENCE ANALYSIS OF THE RLS ALGORITHM 448
9.8 COMPUTER EXPERIMENT ON ADAPTIVE EQUALIZATION 454 9.9 ROBUSTNESS
OTRLS FILTERS 457 9.10 SUMMARY 463 PROBLEMS 463 CONTENTS VII CHAPTER 10
KALMAN FILTERS 466 10.1 RECURSIVE MINIMUM MEAN-SQUARE ESTIMATION FOR
SCALAR RANDOM VARIABLES 466 10.2 STATEMENT OF THE KALMAN FILTERING
PROBLEM 470 10.3 THE INNOVATIONS PROCESS 472 10.4 ESTIMATION OF THE
STATE USING THE INNOVATIONS PROCESS 474 10.5 FILTERING 479 10.6 INITIAL
CONDITIONS 483 10.7 SUMMARY OF THE KALMAN FILTER 483 10.8 KALMAN FILTER
AS THE UNIFYING BASIS FOR RLS FILTERS 485 10.9 VARIANTS OF THE KALMAN
FILTER 491 10.10 THE EXTENDED KALMAN FILTER 496 10.11 SUMMARY 501
PROBLEMS 501 CHAPTER 11 SQUARE-ROOT ADAPTIVE FILTERS 506 11.1
SQUARE-ROOT KALMAN FILTERS 506 11.2 BUILDING SQUARE-ROOT ADAPTIVE
FILTERS ON THEIR KALMAN FILTER COUNTERPARTS 512 11.3 QR-RLS ALGORITHM
513 11.4 ADAPTIVE BEAMFORMING 521 11.5 INVERSE QR-RLS ALGORITHM 528 11.6
SUMMARY 531 PROBLEMS 531 CHAPTER 12 ORDER-RECURSIVE ADAPTIVE FILTERS 535
12.1 GRADIENT-ADAPTIVE LATTICE FILTER 536 12.2 ORDER-RECURSIVE ADAPTIVE
FILTERS USING LEAST-SQUARES ESTIMATION: AN OVERVIEW 543 12.3 ADAPTIVE
FORWARD LINEAR PREDICTION 544 12.4 ADAPTIVE BACKWARD LINEAR PREDICTION
548 12.5 CONVERSION FACTOR 550 12.6 LEAST-SQUARES LATTICE PREDICTOR 553
12.7 ANGLE-NORMALIZED ESTIMATION ERRORS 563 12.8 FIRST-ORDER STATE-SPACE
MODELS FOR LATTICE FILTERING 565 12.9 QR-DECOMPOSITION-BASED
LEAST-SQUARES LATTICE FILTERS 571 12.10 FUNDAMENTAL PROPERTIES OF THE
QRD-LSL FILTER 579 12.11 COMPUTER EXPERIMENT ON ADAPTIVE EQUALIZATION
581 12.12 RECURSIVE LEAST-SQUARES LATTICE FILTERS USING A POSTERIORI
ESTIMATION ERRORS 586 12.13 RECURSIVE LSL FILTERS USING A PRIORI
ESTIMATION ERRORS WITH ERROR FEEDBACK 592 12.14 RELATION BETWEEN
RECURSIVE LSL AND RLS FILTERS 596 12.15 SUMMARY 598 PROBLEMS 600 CHAPTER
13 FINITE-PRECISION EFFECTS 607 13.1 QUANTIZATION ERRORS 608 13.2
LEAST-MEAN-SQUARE ALGORITHM 610 13.3 RECURSIVE LEAST-SQUARES ALGORITHM
619 13.4 SQUARE-ROOT ADAPTIVE FILTERS 625 13.5 ORDER-RECURSIVE ADAPTIVE
FILTERS 627 13.6 FAST TRANSVERSAL FILTERS 629 13.7 * SUMMARY 633
PROBLEMS 635 CHAPTER 14 TRACKING OF TIME-VARYING SYSTEMS 637 14.1 MARKOV
MODEL FOR SYSTEM IDENTIFICATION 637 14.2 DEGREE OF NONSTATIONARITY 640
VIII CONTENTS 14.3 CRITERIA FOR TRACKING ASSESSMENT 642 14.4 TRACKING
PERFORMANCE OF THE LMS ALGORITHM 643 14.5 TRACKING PERFORMANCE OF THE
RLS ALGORITHM 647 14.6 COMPARISON OF THE TRACKING PERFORMANCE OF LMS AND
RLS ALGORITHMS 651 14.7 HOW TO IMPROVE THE TRACKING BEHAVIOR OF THE RLS
ALGORITHM 654 14.8 COMPUTER EXPERIMENT ON SYSTEM IDENTIFICATION 657 14.9
AUTOMATIC TUNING OF THE ADAPTATION CONSTANTS 659 14.10 SUMMARY 664
PROBLEMS 665 CHAPTER 15 ADAPTIVE FILTERS USING INFINITE-DURATION IMPULSE
RESPONSE STRUCTURES 666 15.1 IIR ADAPTIVE FILTERS: OUTPUT ERROR METHOD
666 15.2 IIR ADAPTIVE FILTERS: EQUATION ERROR METHOD 671 15.3 SOME
PRACTICAL CONSIDERATIONS 673 15.4 LAGUERRE TRANSVERSAL FILTERS 674 15.5
ADAPTIVE LAGUERRE LATTICE FILTERS 677 15.6 SUMMARY 681 PROBLEMS 682
CHAPTER 16 BLIND DECONVOLUTION 684 16.1 AN OVERVIEW OF THE BLIND
DECONVOLUTION PROBLEM 684 16.2 CHANNEL IDENTIFIABILITY USING
CYCLOSTATIONARY STATISTICS 688 16.3 SUBSPACE DECOMPOSITION FOR
FRACTIONALLY SPACED BLIND IDENTIFICATION 689 16.4 BUSSGANG ALGORITHM FOR
BLIND EQUALIZATION 703 16.5 EXTENSION OF THE BUSSGANG ALGORITHM TO
COMPLEX BASEBAND CHANNELS 720 16.6 SPECIAL CASES OF THE BUSSGANG
ALGORITHM 721 16.7 FRACTIONALLY SPACED BUSSGANG EQUALIZERS 725 16.8
SUMMARY 729 PROBLEMS 732 CHAPTER 17 BACK-PROPAGATION LEARNING 736 17.1
SIGMOID NEURONAL MODEL 736 17.2 MULTILAYER PERCEPTRON 738 17.3 COMPLEX
BACK-PROPAGATION ALGORITHM 740 17.4 UNIVERSAL APPROXIMATION THEOREM 753
17.5 NETWORK COMPLEXITY 755 17.6 TEMPORAL PROCESSING: HOW TO ACCOUNT FOR
"TIME" 756 17.7 VIRTUES AND LIMITATIONS OF BACK-PROPAGATION LEARNING 758
17.8 SUMMARY 759 PROBLEMS 760 EPILOGUE 762 1. PROPORTIONATE ADAPTATION
762 2. ROBUST STATISTICS 764 3. BLIND SOURCE SEPARATION 766 4. RECURRENT
NEURAL NETWORKS 770 5. NONLINEAR DYNAMICAL SYSTEMS: DERIVATIVE-FREE
STATE ESTIMATION 773 APPENDIX A COMPLEX VARIABLES 779 A.I
CAUCHY-REIMAJIN EQUATIONS 779 A.2 CAUCHY'S INTEGRAL FORMULA 781 A.3
LAURENT'S SERIES 783 CONTENTS IX A.4 SINGULARITIES AND RESIDUES 785 A.5
CAUCHY'S RESIDUE THEOREM 786 A.6 PRINCIPLE OF THE ARGUMENT 787 A.7
INVERSION INTEGRAL FOR THE Z-TRANSFORM 790 A.8 PARSEVAL'S THEOREM 792
APPENDIX B DIFFERENTIATION WITH RESPECT TO A VECTOR 794 B.1 BASIC
DEFINITIONS 794 B.2 EXAMPLES 796 B.3 RELATION BETWEEN THE DERIVATIVE
WITH RESPECT TO A VECTOR AND THE GRADIENT VECTOR 798 APPENDIX C METHOD
OF LAGRANGE MULTIPLIERS 799 C.I OPTIMIZATION INVOLVING A SINGLE EQUALITY
CONSTRAINT 799 C.2 OPTIMIZATION INVOLVING MULTIPLE EQUALITY CONSTRAINTS
800 C.3 OPTIMUM BEAMFORMER 801 APPENDIX D ESTIMATION THEORY 802 D.I
LIKELIHOOD FUNCTION 802 D.2 CRAM6R-RAO INEQUALITY 803 D.3 PROPERTIES OF
MAXIMUM-LIKELIHOOD ESTIMATORS 804 D.4 CONDITIONAL MEAN ESTIMATOR 805
APPENDIX E EIGENANALYSIS 807 E.I THE EIGENVALUE PROBLEM 807 E.2
PROPERTIES OF EIGENVALUES AND EIGENVECTORS 809 E.3 LOW-RANK MODELING 823
E.4 EIGENFILTERS 827 E.5 EIGENVALUE COMPUTATIONS 829 APPENDIX F
ROTATIONS AND REFLECTIONS 833 F.I PLANE ROTATIONS 833 F.2 TWO-SIDED
JACOBI ALGORITHM 835 F.3 CYCLIC JACOBI ALGORITHM 841 F.4 HOUSEHOLDER
TRANSFORMATION 844 F.5 THE QR ALGORITHM 847 APPENDIX G COMPLEX WISHART
DISTRIBUTION 854 G.I DEFINITION 854 G.2 THE CHI-SQUARE DISTRIBUTION AS A
SPECIAL CASE 855 G.3 PROPERTIES OF THE COMPLEX WISHART DISTRIBUTION 856
G.4 EXPECTATION OF THE INVERSE CORRELATION MATRIX ^^(N) 857 GLOSSARY 858
BIBLIOGRAPHY 870 INDEX 912 |
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| dewey-search | 621.3815/324 |
| dewey-sort | 3621.3815 3324 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Informatik Elektrotechnik Elektrotechnik / Elektronik / Nachrichtentechnik |
| discipline_str_mv | Informatik Elektrotechnik Elektrotechnik / Elektronik / Nachrichtentechnik |
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| id | DE-604.BV021964232 |
| illustrated | Illustrated |
| index_date | 2024-07-02T16:08:44Z |
| indexdate | 2024-07-09T20:48:22Z |
| institution | BVB |
| isbn | 0130901261 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015179382 |
| oclc_num | 46538540 |
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| owner | DE-706 DE-573 DE-29T DE-91 DE-BY-TUM DE-703 DE-1050 DE-384 DE-Aug4 DE-19 DE-BY-UBM DE-83 DE-M100 |
| owner_facet | DE-706 DE-573 DE-29T DE-91 DE-BY-TUM DE-703 DE-1050 DE-384 DE-Aug4 DE-19 DE-BY-UBM DE-83 DE-M100 |
| physical | XVI, 920 S. graph. Darst. |
| publishDate | 2002 |
| publishDateSearch | 2002 |
| publishDateSort | 2002 |
| publisher | Prentice Hall |
| record_format | marc |
| series2 | Prentice Hall information and systems sciences series |
| spelling | Haykin, Simon S. 1931- Verfasser (DE-588)128698497 aut Adaptive filter theory Simon Haykin 4. ed. Upper Saddle River, NJ Prentice Hall 2002 XVI, 920 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Prentice Hall information and systems sciences series Literaturverz. S. 870 - 911 Circuitos eletrônicos larpcal Filtros elétricos larpcal Adaptive filters Adaptives Filter (DE-588)4141377-5 gnd rswk-swf Adaptives Filter (DE-588)4141377-5 s DE-604 HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015179382&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Haykin, Simon S. 1931- Adaptive filter theory Circuitos eletrônicos larpcal Filtros elétricos larpcal Adaptive filters Adaptives Filter (DE-588)4141377-5 gnd |
| subject_GND | (DE-588)4141377-5 |
| title | Adaptive filter theory |
| title_auth | Adaptive filter theory |
| title_exact_search | Adaptive filter theory |
| title_exact_search_txtP | Adaptive filter theory |
| title_full | Adaptive filter theory Simon Haykin |
| title_fullStr | Adaptive filter theory Simon Haykin |
| title_full_unstemmed | Adaptive filter theory Simon Haykin |
| title_short | Adaptive filter theory |
| title_sort | adaptive filter theory |
| topic | Circuitos eletrônicos larpcal Filtros elétricos larpcal Adaptive filters Adaptives Filter (DE-588)4141377-5 gnd |
| topic_facet | Circuitos eletrônicos Filtros elétricos Adaptive filters Adaptives Filter |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015179382&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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